From Interactive to Adaptive Augmented Reality

This paper presents several results from the research department "Augmented Vision" of the German Research Center for Artificial Intelligence. The driving idea of this work is to move from traditional Augmented Reality (AR) systems, which are often limited to visualization and tracking components, to AR cognitive systems, which have or gradually build knowledge about the situation and intentions of the user. Such systems will basically be much more unobtrusive and adapt the information presentation to the users' actual needs. To reach this goal, strong progress must be done in several areas, starting with 3D scene digitalization and analysis, body modeling and motion capturing, and action and workflow recognition. An overview of current results and work-in-progress of the Augmented Vision group in those areas is presented and finally discussed.

[1]  Gustaf Hendeby,et al.  Using egocentric vision to achieve robust inertial body tracking under magnetic disturbances , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[2]  Didier Stricker,et al.  Dense 3D Point Cloud Generation from Multiple High-resolution Spherical Images , 2011, VAST.

[3]  Didier Stricker,et al.  SIFT in perception-based color space , 2010, 2010 IEEE International Conference on Image Processing.

[4]  Didier Stricker,et al.  Robust Outlier Removal from Point Clouds Acquired with Structured Light , 2012, Eurographics.

[5]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Didier Stricker,et al.  Robust Point Matching in HDRI through Estimation of Illumination Distribution , 2011, DAGM-Symposium.

[7]  Didier Stricker,et al.  Advanced tracking through efficient image processing and visual-inertial sensor fusion , 2008, 2008 IEEE Virtual Reality Conference.

[8]  Didier Stricker,et al.  Structure from Motion using full spherical panoramic cameras , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[9]  Didier Stricker,et al.  Real-time vision-based tracking and reconstruction , 2007, Journal of Real-Time Image Processing.

[10]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[11]  Andrew Zisserman,et al.  Multiple View Geometry , 1999 .

[12]  Laura Rocchi,et al.  Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors , 2008, Medical & Biological Engineering & Computing.

[13]  Didier Stricker,et al.  Activity Recognition Using Biomechanical Model Based Pose Estimation , 2010, EuroSSC.

[14]  Didier Stricker,et al.  Algorithms for 3D Shape Scanning with a Depth Camera , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Didier Stricker,et al.  Efficient Packing of Arbitrary Shaped Charts for Automatic Texture Atlas Generation , 2011, EGSR '11.

[16]  Didier Stricker,et al.  Learning task structure from video examples for workflow tracking and authoring , 2012, 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[17]  Didier Stricker,et al.  3D Body Scanning With One Kinect , 2011 .

[18]  Tomás Pajdla,et al.  Structure from motion with wide circular field of view cameras , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.